Improving Multi-Agent Generative Adversarial Nets with Variational Latent Representation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Generative Adversarial Networks (GANs) are powerful deep generative models.
• A significant challenge in GANs is the problem of mode collapse, limiting sample diversity.

Purpose of the Study:

• To introduce a novel GAN architecture, the encoded multi-agent generative adversarial network (E-MGAN).
• To address and mitigate the mode collapse problem in generative modeling.

Main Methods:

• E-MGAN integrates variational latent representations from a Variational Auto-Encoder (VAE) into a multi-agent GAN framework.
• The generator utilizes multiple generators and is penalized by a classifier, replacing traditional random noise input with learned representations.
• Experiments were conducted on synthetic and large-scale real-world datasets.

Main Results:

• The E-MGAN model demonstrated improved quality and diversity of generated samples.
• Quantitative assessments using Inception Score (IS) and Fréchet Inception Distance (FID) showed superior performance.
• Qualitative evaluation through sample visualization confirmed the model's effectiveness.

Conclusions:

• The proposed E-MGAN effectively overcomes the mode collapse issue in GANs.
• E-MGAN offers enhanced sample quality and diversity compared to existing state-of-the-art GAN variants.
• This approach represents a significant advancement in generative modeling techniques.